Artificial intelligence methods and systems for constitutional analysis using objective functions

ABSTRACT

A system for constitutional analysis using objective functions includes a computing device configured to generate a ranked list of diseases, by determining a plurality of disease impact score vectors for plurality of diseases, and generating and optimizing a first objective function of the impact score vectors, to receive, from a user, a plurality of user physiological history data, to identify, as a function of a disease state classifier, a plurality of disease states associated with the plurality of user physiological history data, to match at least a disease state of the plurality of disease states to the ranked list of diseases, and to generate a curative habitual pattern to alleviate the at least a disease state by combining intervention elements to form a curative habitual pattern candidates, calculating a curative impact score of each curative habitual pattern candidate, and selecting the curative habitual pattern using the curative impact score.

FIELD OF THE INVENTION

The present invention generally relates to the field of objectivefunction optimization. In particular, the present invention is directedto methods and systems for constitutional analysis using objectivefunctions.

BACKGROUND

Artificial intelligence methods are increasingly valuable for analysisof patterns in large quantities of data. However, where the data islarge and varied enough, tradeoffs between sophistication and efficiencycan become untenable.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for constitutional analysis using objectivefunctions includes a computing device configured to generate a rankedlist of diseases, wherein generating further comprises determining aplurality of disease impact score vectors associated with a plurality ofdiseases, wherein each disease impact score vector includes at least adisease impact score, generating a first objective function of theimpact score vectors, and ranking the diseases according to optimizationof the first objective function. Computing device is configured toreceive, from a user, a plurality of user physiological history data.Computing device is configured to identify, as a function of a diseasestate classifier, a plurality of disease states associated with theplurality of user physiological history data. Computing device isconfigured to match at least a disease state of the plurality of diseasestates to the ranked list of diseases. Computing device is configured togenerate a curative habitual pattern to alleviate the at least a diseasestate, wherein generating further includes forming a query using the atleast a disease state, retrieving, from an intervention listingdatastore, a plurality of intervention elements as a function of thequery, combining the plurality of intervention elements to form aplurality of curative habitual pattern candidates, calculating acurative impact score of each curative habitual pattern candidate of theplurality of curative habitual pattern candidates, and selecting thecurative habitual pattern from the plurality of curative habitualpattern candidates as a function of the curative impact score.

In another aspect a method of constitutional analysis using objectivefunctions includes generating, by a computing device, a ranked list ofdiseases, wherein generating further includes determining a plurality ofdisease impact score vectors associated with a plurality of diseases,wherein each disease impact score vector includes at least a diseaseimpact score, generating a first objective function of the impact scorevectors, and ranking the diseases according to optimization of the firstobjective function. The method includes receiving, by the computingdevice and from a user, a plurality of user physiological history data.The method includes identifying, by the computing device and as afunction of a disease state classifier, a plurality of disease statesassociated with the plurality of user physiological history data. Themethod includes matching, by the computing device, at least a diseasestate of the plurality of disease states to the ranked list of diseases.The method includes generating, by the computing device, a curativehabitual pattern to alleviate the at least a disease state, whereingenerating further includes forming a query using the at least a diseasestate, retrieving, from an intervention listing datastore, a pluralityof intervention elements as a function of the query, combining theplurality of intervention elements to form a plurality of curativehabitual pattern candidates, calculating a curative impact score of eachcurative habitual pattern candidate of the plurality of curativehabitual pattern candidates, and selecting the curative habitual patternfrom the plurality of curative habitual pattern candidates as a functionof the curative impact score.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for constitutional analysis using objective functions;

FIG. 2 is a block diagram illustrating an exemplary embodiment of anexpert database;

FIG. 3 is a flow diagram illustrating a method of constitutionalanalysis using objective functions; and

FIG. 4 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations, and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, embodiments described herein improve speed and accuracyin analysis of constitutional and/or physiological data by selecting asubset of maximally impactful conditions and restricting analysis tooutputs pertaining to the subset. Objective functions may be used toselect the subset based on numerical scoring derived frommachine-learning processes. Further classification of physiological datato conditions may enable detection and alleviation thereof in users.

Referring now to FIG. 1, an exemplary embodiment of a system 100 forconstitutional analysis using objective functions is illustrated. Systemincludes a computing device 104. Computing device 104 may include anycomputing device 104 as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure.Computing device 104 may include, be included in, and/or communicatewith a mobile device such as a mobile telephone or smartphone. Computingdevice 104 may include a single computing device 104 operatingindependently or may include two or more computing device 104 operatingin concert, in parallel, sequentially or the like; two or more computingdevices may be included together in a single computing device 104 or intwo or more computing devices. Computing device 104 may interface orcommunicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting computing device 104 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device 104.Computing device 104 may include but is not limited to, for example, acomputing device 104 or cluster of computing devices in a first locationand a second computing device 104 or cluster of computing devices in asecond location. Computing device 104 may include one or more computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and the like. Computing device 104 may distribute one ormore computing tasks as described below across a plurality of computingdevices of computing device 104, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between computing devices. Computing device 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device 104.

Computing device 104 may be designed and/or configured to perform anymethod, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, computing device 104 may be configured toperform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Computing device 104 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 1, computing device 104 is configured togenerate a ranked list 108 of diseases. In an embodiment, computingdevice 104 may generate ranked list 108 by determining a plurality ofdisease impact score vectors associated with a plurality of diseases.Each disease impact score vector includes at least a disease impactscore. A “disease impact score,” as used in this disclosure, is a scoremeasuring an overall impact of a disease in reduction of longevity andquality of life. For instance, and without limitation, a disease impactscore may measure a likely reduction in lifespan due to a correspondingdisease; for instance, atherosclerosis may remove some average number ofyears from a person's life expectancy, while a certain type of cancermay remove a different average number of years; a larger reduction inlifespan may be given a higher impact score. An additional example of animpact score may be a score assessing a degree of disability, asmeasured in years of disability, severity of disability, and/orprogression of disability, for instance to assess a degree to which thedisease makes full enjoyment of life difficult or impossible. Forinstance, and without limitation, a condition such as without limitationosteoarthritis, rheumatoid arthritis, congestive heart failure, multiplesclerosis, or some cancers may be degenerative, and may graduallyincrease debility leading to a number of years of extreme disability;such a condition may receive a higher score than a condition that doesnot cause a lengthy period of severe disability. A further exemplaryimpact score may be based on an age of onset of a disease, where anearlier age of onset may be associated with a higher impact score. Anadditional exemplary impact score may relate to an average age at death.Another exemplary impact score may describe a frequency of a conditionand/or deaths therefrom within a population; for instance, heartdisease, chronic lower respiratory illnesses, cerebrovascular disease,and Alzheimer's disease may all receive high impact scores related tofrequency. A further exemplary impact score may measure a degree tosusceptibility of improvement through curative habitual patterns asdescribed below, such as improvements to diet, nutrition, fitness,emotional health, or the like, where a disease more easily preventedand/or alleviated through such interventions, such as type II diabetesor heart disease, may have a higher score than a disease such asHuntington's disease, which may not be materially affected by suchinterventions. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various alternative or additionalimpact scores that may be used consistently with this disclosure.

With continued reference to FIG. 1, a “vector” as defined in thisdisclosure is a data structure that represents one or more aquantitative values and/or measures such as disease impact scores. Avector may be represented as an n-tuple of values, where n is at leasttwo values, as described in further detail below; a vector mayalternatively or additionally be represented as an element of a vectorspace, defined as a set of mathematical objects that can be addedtogether under an operation of addition following properties ofassociativity, commutativity, existence of an identity element, andexistence of an inverse element for each vector, and can be multipliedby scalar values under an operation of scalar multiplication compatiblewith field multiplication, and that has an identity element isdistributive with respect to vector addition, and is distributive withrespect to field addition. Each value of n-tuple of values may representa measurement or other quantitative value associated with a givencategory of data, or attribute, examples of which are provided infurther detail below; a vector may be represented, without limitation,in n-dimensional space using an axis per category of value representedin n-tuple of values, such that a vector has a geometric directioncharacterizing the relative quantities of attributes in the n-tuple ascompared to each other. Two vectors may be considered equivalent wheretheir directions, and/or the relative quantities of values within eachvector as compared to each other, are the same; thus, as a non-limitingexample, a vector represented as [5, 10, 15] may be treated asequivalent, for purposes of this disclosure, as a vector represented as[1, 2, 3]. Vectors may be more similar where their directions are moresimilar, and more different where their directions are more divergent;however, vector similarity may alternatively or additionally bedetermined using averages of similarities between like attributes, orany other measure of similarity suitable for any n-tuple of values, oraggregation of numerical similarity measures for the purposes of lossfunctions as described in further detail below. Any vectors as describedherein may be scaled, such that each vector represents each attributealong an equivalent scale of values. Each vector may be “normalized,” ordivided by a “length” attribute, such as a length attribute l as derivedusing a Pythagorean norm:

${l = \sqrt{\sum\limits_{i = 0}^{n}\; a_{i}^{2}}},$where α_(i) is attribute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes.

Still referring to FIG. 1, computing device 104 may determine aplurality of impact score vectors and/or disease impact scores byvarious methods, including receiving statistics reported from healthreporting agencies and/or devices operated thereby, for instancedescribing age of onset and/or age of death from various diseases,statistical analysis of data describing such diseases, or the like.Receiving data describing such diseases may include receiving diseaseimpact training data 112. “Training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, training data may include one or moreelements that are not categorized; that is, training data may not beformatted or contain descriptors for some elements of data.Machine-learning processes and/or other processes may sort training dataaccording to one or more categorizations using, for instance, naturallanguage processing algorithms, tokenization, detection of correlatedvalues in raw data and the like; categories may be generated usingcorrelation and/or other processing algorithms. As a non-limitingexample, in a corpus of text, phrases making up a number “n” of compoundwords, such as nouns modified by other nouns, may be identifiedaccording to a statistically significant prevalence of n-gramscontaining such words in a particular order; such an n-gram may becategorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name may be identified by reference to a list,dictionary, or other compendium of terms, permitting ad-hoccategorization by machine-learning algorithms, and/or automatedassociation of data in the data entry with descriptors or into a givenformat. The ability to categorize data entries automatedly may enablethe same training data to be made applicable for two or more distinctmachine-learning algorithms as described in further detail below.Training data used by computing device 104 may correlate any input dataas described in this disclosure to any output data as described in thisdisclosure.

As a non-limiting example, and with continued reference to FIG. 1,disease impact training data 112 including a plurality of entriescorrelating diseases of the plurality of diseases with disease impactscores, where correlation with disease impact scores may includecorrelation with data convertible to impact scores; for instance, anumber of years lost, and/or a set of parameters such as life expectancyand age of death from a disease that may be mathematically equivalent,may be treated as convertible to an impact score measuring an expectedand/or average loss of lifespan.

Still referring to FIG. 1, disease impact training data 112 may bereceived and/or collated from various sources. For instance, a source ofdisease impact training data 112 may include one or more reportingagencies such as the Center for Disease Control (CDC), NationalInstitute of Health (NIH) or the like. Alternatively or additionally,disease impact training data 112 may be received in the form of one ormore expert inputs. Expert inputs may be received directly and/or storedin an expert database 116. Expert database 116 may be implemented,without limitation, as a relational expert database 116, a key-valueretrieval expert database 116 such as a NOSQL expert database 116, orany other format or structure for use as an expert database 116 that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. Expert database 116 may alternatively oradditionally be implemented using a distributed data storage protocoland/or data structure, such as a distributed hash table or the like.Expert database 116 may include a plurality of data entries and/orrecords as described above. Data entries in an expert database 116 maybe flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationalexpert database 116. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which dataentries in an expert database 116 may store, retrieve, organize, and/orreflect data and/or records as used herein, as well as categories and/orpopulations of data consistently with this disclosure.

Referring now to FIG. 2, an exemplary embodiment of an expert database116 is illustrated. Expert database 116 may, as a non-limiting example,organize data stored in the expert database 116 according to one or moredatabase tables. One or more database tables may be linked to oneanother by, for instance, common column values. For instance, a commoncolumn between two tables of expert database 116 may include anidentifier of an expert submission, such as a form entry, textualsubmission, expert paper, or the like, for instance as defined below; asa result, a query may be able to retrieve all rows from any tablepertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of expert data, names and/or identifiers ofexperts submitting the data, times of submission, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which expert data from one or more tablesmay be linked and/or related to expert data in one or more other tables.

Still referring to FIG. 2, one or more database tables in expertdatabase 116 may include, as a non-limiting example, a disease impacttable 200. Disease impact table 200 may list disease impact scoresand/or quantities suitable for calculation thereof, as reported byexperts. For instance, and without limitation, columns listed in diseaseimpact table may correspond to net effect on life expectancy, degree ofdisability, age of onset, frequency within population, and/or otherelements suitable for use as an impact score. One or more databasetables in expert database 116 may include, as a non-limiting example, adisease alleviation table 204. Disease alleviation table 204 may containentries associating each disease of a plurality of disease with curativehabitual patterns, as defined below, identified by experts as affectingimpact of a subject disease, and/or a degree of reduction in any impactscore, such as a probability of prevention, reduction of lifespan impact(i.e. years of lifespan regained by use of curative habitual patterns),reduction of disability, or the like.

In an embodiment, and still referring to FIG. 2, a forms processingmodule 208 may sort data entered in a submission via a graphical userinterface 212 receiving expert submissions by, for instance, sortingdata from entries in the graphical user interface 212 to relatedcategories of data; for instance, data entered in an entry relating inthe graphical user interface 212 to disease impacts may be sorted intovariables and/or data structures for impact score data, which may beprovided to disease impact 200, while data entered in an entry relatingto alleviation of disease using curative habitual patterns may be sortedinto variables and/or data structures for the storage of such data, suchas disease alleviation table 204. Where data is chosen by an expert frompre-selected entries such as drop-down lists, data may be storeddirectly; where data is entered in textual form, a language processingmodule may be used to map data to an appropriate existing label, forinstance using a vector similarity test or other synonym-sensitivelanguage processing test to map data to existing labels and/orcategories. Similarly, data from an expert textual submissions 216, suchas accomplished by filling out a paper or PDF form and/or submittingnarrative information, may likewise be processed using languageprocessing module, which may be implemented, without limitation asdescribed in U.S. Nonprovisional Application Ser. No. 16/372,512, filedon Apr. 2, 2019, and entitled METHODS AND SYSTEMS FOR UTILIZINGDIAGNOSTICS FOR INFORMED VIBRANT CONSTITUTIONAL GUIDANCE, the entiretyof which is incorporated herein by reference.

Data may be extracted from expert papers 224, which may include withoutlimitation publications in medical and/or scientific journals, bylanguage processing module 220 via any suitable process as describedherein. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional methods whereby novelterms may be separated from already-classified terms and/or synonymstherefore, as consistent with this disclosure.

In an embodiment, and referring again to FIG. 1, disease impact trainingdata 112 may be limited to one or more categories of users and/orpersons. For instance, and without limitation, one population of peopledefined by one or more demographic traits may be impacted differently byone disease than by another; as an example, certain cancers, such asprostate cancer, may be far more prevalent in men than in women, whilesome autoimmune conditions may be far more prevalent in women than inmen. In addition to sex, further categories to which training data maybe limited may include, without limitation, ethnicity, national origin,age, blood type, or the like. Training data may be limited tointersections and/or unions of such categories, so that for instance,training data may be limited for some purposes to Caucasian women above40, or the like; some of the same elements training data may also besorted separately to two or more categories in successive sortingprocesses, such that for instance data relating to a Caucasian woman or,set of Caucasian women, over 40 may also be sorted to a category ofCaucasian people, people over 40, and/or women. Sorting of training datainto categories may be performed using at least a user classifier 120.Categories may be listed in expert database 116 and/or received asexpert submissions; for instance, experts and/or expert submissions mayidentify statistically useful categories for the purposes of impactscore determination.

Further referring to FIG. 1, a “classifier,” such as without limitationa user classifier 120, is a machine-learning model, such as amathematical model, neural net, or program generated by a machinelearning algorithm known as a “classification algorithm,” as describedin further detail below, that sorts inputs into categories or bins ofdata, outputting the categories or bins of data and/or labels associatedtherewith. A classifier may be configured to output at least a datumthat labels or otherwise identifies a set of data that are clusteredtogether, found to be close under a distance metric as described below,or the like. Computing device 104 and/or another device may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device 104 derives a classifier from training data.Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers.

Still referring to FIG. 1, computing device 104 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)+P(B), where P(AB) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Computingdevice 104 may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Computing device 104 may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1, computing device 104 may beconfigured to generate a classifier using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample-features resemble training data toclassify input data to one or more clusters and/or categories offeatures as represented in training data; this may be performed byrepresenting both training data and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

With continued reference to FIG. 1, generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm:

${l = \sqrt{\sum\limits_{i = 0}^{n}\; a_{i}^{2}}},$where α_(i) is attribute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes; this may, for instance, be advantageous wherecases represented in training data are represented by differentquantities of samples, which may result in proportionally equivalentvectors with divergent values.

Still referring to FIG. 1, computing device 104 may train a diseaseimpact model 124 as a function of the disease impact training data 112and a machine-learning process and determine the plurality of impactscore vectors as a function of the disease impact model 124. A “machinelearning process,” as used in this disclosure, is a process thatautomatedly uses a body of data known as “training data” and/or a“training set” to generate an process that will be performed by acomputing device 104/module to produce outputs given data provided asinputs; this is in contrast to a non-machine learning software programwhere the commands to be executed are determined in advance by a userand written in a programming language.

With continued reference to FIG. 1, machine-learning process may includewithout limitation a regression process such as a linear regressionprocess. Linear regression models may include ordinary least squaresregression, which aims to minimize the square of the difference betweenpredicted outcomes and actual outcomes according to an appropriate normfor measuring such a difference (e.g. a vector-space distance norm);coefficients of the resulting linear equation may be modified to improveminimization. Linear regression models may include ridge regressionmethods, where the function to be minimized includes the least-squaresfunction plus term multiplying the square of each coefficient by ascalar amount to penalize large coefficients. Linear regression modelsmay include least absolute shrinkage and selection operator (LASSO)models, in which ridge regression is combined with multiplying theleast-squares term by a factor of 1 divided by double the number ofsamples. Linear regression models may include a multi-task lasso modelwherein the norm applied in the least-squares term of the lasso model isthe Frobenius norm amounting to the square root of the sum of squares ofall terms. Linear regression models may include the elastic net model, amulti-task elastic net model, a least angle regression model, a LARSlasso model, an orthogonal matching pursuit model, a Bayesian regressionmodel, a logistic regression model, a stochastic gradient descent model,a perceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 1, machine-learning process may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 1, models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training dataset are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data.

Still referring to FIG., machine-learning process may include at least asupervised machine-learning algorithm. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm mayinclude diseases, and/or labels identifying diseases, as described aboveas inputs, impact scores as outputs, and a scoring function representinga desired form of relationship to be detected between inputs andoutputs; scoring function may, for instance, seek to maximize theprobability that a given input and/or combination of elements inputs isassociated with a given output to minimize the probability that a giveninput is not associated with a given output. Scoring function may beexpressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various possiblevariations of supervised machine learning algorithms that may be used todetermine relation between inputs and outputs.

Further referring to FIG. 1, disease impact model 124 may output eachimpact score for each disease of plurality of diseases; each impactscore may be calculated per disease. In an embodiment, disease treatmentmodel may include a model per category of user category according towhich trading data is sorted as described above, generating a pluralityof category-specific disease impact models 124.

Still referring to FIG. 1, computing device 104 is configured togenerate a first objective function 128 of the impact score vectors. An“objective function,” as used in this disclosure, is a mathematicalfunction used by a computing device 104 to score a quantitative elementsuch as an impact score vector, which may include one impact scoreand/or a plurality of objective scores. In various embodiments a scoreof a particular impact scored vector may be based on a combination ofone or more factors, including impact scores. Each impact score vectormay be assigned a score based on predetermined variables. In someembodiments, the assigned scores may be weighted or unweighted.Computing device 104 may compute a score associated with each impactscore vector and select impact score vectors to minimize and/or maximizethe score, depending on whether an optimal result is represented,respectively, by a minimal and/or maximal score.

Continuing to refer to FIG. 1, computing device 104 is configured toproduce ranked list 108 by ranking diseases according to optimization offirst objective function 128. Objective function may be formulated as alinear objective function, which computing device 104 may solve using alinear program such as without limitation a mixed-integer program. A“linear program,” as used in this disclosure, is a program thatoptimizes a linear objective function, given at least a constraint. Forinstance, a lifespan impact may be constrained to less than anactuarially predicted year of death minus age of onset, while an amountof years of lifespan recoverable using a curative habitual pattern, asdescribed in further detail below, may be constrained to less than thelifespan impact. In various embodiments, system 100 may determine impactscore vector that maximizes a total score subject to at least aconstraint. A mathematical solver may be implemented to solve for theset disease impact vectors that maximizes scores; mathematical solvermay implemented on computing device 104 and/or another device in system100, and/or may be implemented on third-party solver. A higher scoremay, for instance, be given to a disease that has a high lifespanimpact, an impact score indicating a high degree of disability caused bythe disease, and a high impact score relating to susceptibility forimprovement using curative habitual patterns.

With continued reference to FIG. 1, optimizing objective function mayinclude minimizing a loss function, where a “loss function” is anexpression an output of which an optimization algorithm minimizes togenerate an optimal result. As a non-limiting example, computing device104 may assign variables relating to a set of parameters, which maycorrespond to score components as described above, calculate an outputof mathematical expression using the variables, and select a diseaseimpact vector that produces an output having the lowest size, accordingto a given definition of “size,” of the set of outputs representing eachof plurality of candidate ingredient combinations; size may, forinstance, included absolute value, numerical size, or the like.Selection of different loss functions may result in identification ofdifferent disease impact vectors as generating minimal outputs.

Still referring to FIG. 1, computing device 104 may rank diseasesaccording to degree to which corresponding disease impact vectorsoptimize first objective function 128; this may result in a ranked list108 in which higher-ranking diseases are more common, result in agreater average loss of life expectancy, result in a greater averagedegree and/or duration of disability, and/or are more susceptible toimprovement through curative habitual patterns, than other entries onthe list. Computing device 104 may be configured to eliminate from theranked list 108 all but a number of top-ranking diseases on the list,where the number may be, without limitation 100, 300, or the like;alternatively or additionally, computing device 104 may compare anobjective function output of each of plurality of devices to apreconfigured threshold number and/or ranking, and eliminate from rankedlist 108 all disease having associated objective function outputsfailing threshold by being, depending on the form of the thresholdcomparison and objective function, greater than or less than thethreshold. Addressing diseases on ranked list 108, using curativehabitual patterns, in descending order of ranking may produce a maximallikely increase in lifespan and/or years of minimal disability for auser.

Still referring to FIG. 1, computing device 104 is configured to receivea plurality of user physiological history data from a user. “Userphysiological history data,” as used in this disclosure, is any medicaland/or health history data pertaining to a user. An element of userphysiological history data may include a user reported an element ofuser physiological history data. A user reported element of userphysiological history data may include any medical data pertaining to auser, supplied by a user. For example, a user reported element of userphysiological history data may include any previous health history,health records, diagnosis, medications, treatments, major surgeries,complications, and the like that the user may be suffering from. Forexample, a user reported an element of user physiological history datamay include an anaphylactic reaction to all tree nuts that the user wasdiagnosed with as a young child. In yet another non-limiting example, auser reported element of user physiological history data may describe aprevious diagnosis such as endometriosis that the user was diagnosedwith three years back, and treatments that the user engages in to manageher endometriosis, including supplementation with fish oil and followinga gluten free diet. In yet another non-limiting example, a user mayprovide one or more elements of health history information, such as whena user may select how much of a user's medical records the user seeks toshare with computing device 104. For example, a user may prefer to shareonly the user's hospitalization records and not the user's currentmedication list. In yet another non-limiting example, a user may seek toshare as many records as are available for the user, such as the user'sentire vaccination history. In yet another non-limiting example, a usermay share health history information that is available to the user, suchas when records may become lost or misplaced. An element of userphysiological history data may include an amount of information orcertain records based on a user's entire medical record that the userseeks to share and allow system 100 and/or a computing device 104 tohave access to. For example, a user may prefer to share only the user'shospitalization records and not the user's current medication list. Inyet another non-limiting example, a user may seek to share as manyrecords as are available for the user, such as the user's entire healthhistory. In yet another non-limiting example, a user may not wish toshare any information pertaining to a user's health history. In yetanother non-limiting example, a user may be unable to share anyinformation pertaining to a user's health history, because the user maybe adopted and may not have access to health records, or the user isunable to locate any health records for the user and the like.

With continued reference to FIG. 1, an element of user physiologicalhistory data may include a user reported self-assessment. A“self-assessment” as used in this disclosure, is any questionnaire thatmay prompt and/or ask a user for any element of user health history. Forinstance and without limitation, a self-assessment may seek to obtaininformation including demographic information such as a user's fulllegal name, sex, date of birth, marital status, date of last physicalexam and the like. A self-assessment may seek to obtain informationregarding a user's childhood illness such as if the user suffered frommeasles, mumps, rubella, chickenpox, rheumatic fever, polio, and thelike. A self-assessment may seek to obtain any vaccination informationand dates a user received vaccinations such as tetanus, hepatitis,influenza, pneumonia, chickenpox, measles mumps and rubella (MMR), andthe like. A self-assessment may seek to obtain any medical problems thatother doctors and/or medical professionals may have diagnosed. Aself-assessment may seek to obtain any information about surgeries orhospitalizations the user experienced. A self-assessment may seek toobtain information about previously prescribed drugs, over-the-counterdrugs, supplements, vitamins, and/or inhalers the user was prescribed. Aself-assessment may seek to obtain information regarding a user's healthhabits such as exercise preferences, nutrition and diet that a userfollows, caffeine consumption, alcohol consumption, tobacco use,recreational drug use, sexual health, personal safety, family healthhistory, mental health, other problems, other remarks, informationpertaining to women only, information pertaining to men only and thelike.

Alternatively or additionally, and further referring to FIG. 1,plurality of user physiological history data may include biologicalextraction data. Biological extraction data may include any data used asa biological extraction as described in U.S. Nonprovisional applicationSer. No. 16/502,835, filed on Jul. 3, 2019, and entitled “METHODS ANDSYSTEMS FOR ACHIEVING VIBRANT CONSTITUTION BASED ON USER INPUTS,” theentirety of which is incorporated herein by reference.

Still referring to FIG. 1, computing device 104 may identify a pluralityof disease states associated with the plurality of user physiologicalhistory data as a function of a disease state classifier 132. A “diseasestate,” as used in this disclosure, is a current diagnosis and/orprediction of a future diagnosis of a disease as described above; forinstance a disease state may include a current diagnosis of diabetes, acurrent classification as “prediabetic,” and/or an identification thatuser may be at risk for a diagnosis of diabetes in the future owing tobody mass index, family history, diet, genetic markers, or the like.Disease state classifier 132 may be implemented, without limitation,using any classification process suitable for implementation of userclassifier 120 as described above. Training data for disease stateclassifier 132 may include a plurality of entries including userphysiological history data and labels of disease states. Training datamay be received, without limitation, from expert submissions and/orexpert database 116, from case histories, and/or records of past dataprocessed by system 100 and/or systems and/or devices in communicationtherewith; for instance a user that submitted user physiological historydata, as described above to system may have been concurrently and/orsubsequently recorded as having suffered from one or more diseases,which data may be used to generate one or more training data entries fortraining disease state classifier 132. In an embodiment, user may beclassified to one or more categories of users as described above, andtraining data for disease state classifier 132 may be limited totraining data corresponding to the one or more categories of users.Computing device 104 and/or any other device may train disease stateclassifier 132 using training data.

With continued reference to FIG. 1, computing device 104 may be furtherconfigured to computing one or more elements of data predicting anyimpact score, as defined above for any disease state as applied to user;this may be performed using training data correlating elements of userphysiological history data with impact score quantities, for instancebased on expert inputs and/or data from an expert database 116 asdescribed above, and/or using data records collected in iterations ofmethods described in this disclosure, related processes performed bysystem 100, and/or information entered by one or more users describingoutcomes of one or more methods as described herein when practiced byusers. Training data may be used to train a user impact score modelusing any machine-learning process as described above, to whichcomputing device 104 may input user physiological data received frominstant user to receive from the model user-specific impact scores. Forinstance, computing device 104 may further be configured to determine anage of onset of each disease state of plurality of disease states, wherethe age of onset is an actual and/or predicted age of onset of a diseasestate for the user. Computing device 104 may be further configured todetermine a likelihood of each disease state of plurality of diseasestates; for instance, where a disease state is a possible or likelyfuture disease user may suffer, computing device 104 may determine aprobability that the user will actually suffer and/or is currentlysuffering from the disease; this may be performed using training datacorrelating elements of user physiological history data withprobabilities of suffering disease states, for instance based on expertinputs and/or data from an expert database 116 as described above,and/or using data records collected in iterations of methods describedin this disclosure, related processes performed by system 100, and/orinformation entered by one or more users describing outcomes of one ormore methods as described herein when practiced by users. Training datamay be used to train a disease state probability model using anymachine-learning process as described above, to which computing device104 may input user physiological data received from instant user toreceive from the model user-specific impact scores. Probabilities ofsuffering a current and/or future disease, as well as other impact scoredata, may alternatively or additionally be determined as described inU.S. Nonprovisional application Ser. No. 16/502,835. In an embodiment,computing device 104 may weight ranked list 108 used for user asdescribed above; weighting may include multiplying the likelihood of thedisease state by an output of the first objective function 128 for acorresponding disease of the plurality of diseases. Ranked list 108 maybe re-ranked according to weighting, prior to subsequent steps performedby computing device 104.

Further referring to FIG. 1, computing device 104 is configured to matchat least a disease state of the plurality of disease states to theranked list 108 of diseases. Matching may include comparing labels ofdisease states to labels of diseases on ranked list 108 of diseases; forinstance, disease states and corresponding diseases may share identicallabels. In an embodiment, computing device 104 may classify user to oneor more categories of users and at least a ranked list 108 computed, asabove, for the one or more categories may be used for matching. Forinstance, user may be classified to one or more categories of user as afunction of user classifier 120, to which computing device 104 may inputone or more elements of user data such as without limitation userdemographic data and/or user physiological data as described above;where user is classified to more than one category in one or more userclassifiers 120 as described above, a category may be selected as a mostsignificant category, and a ranked list 108 associated therewith may beused. Alternatively or additionally, ranked lists 108 corresponding to aplurality of categories matching user may be averaged or otherwiseaggregated, for instance by averaging and/or aggregating ranking foreach disease on lists, to produce a user-specific ranked list 108.

Still referring to FIG. 1, computing device 104 is configured togenerate a curative habitual pattern to alleviate the at least a diseasestate. A “curative habitual pattern,” as used in this disclosure is aset of regularly applied fitness, nutrition, and/or dietary actionstending to improve, prevent, and/or alleviate one or more diseasestates. A curative habitual pattern may include, without limitation acurative nutritional pattern, which is a curative habitual pattern withregard to nutritional input. Curative nutritional pattern may includeregular consumption, cessation of consumption, or consumption ofparticular quantities of food and/or supplements. Curative nutritionalpattern may include regulation of calories or other aggregate measuresof nutritional intake. Curative nutritional pattern may includeregulations of particular nutrient quantities to be consumed. Curativenutritional pattern may include one or more schedules for fasting, forinstance in patterns, schedules and/or plans that integrate foodconsumed, fasting, sleep, exercise, supplementation, current diseasestate, and the like for optimal fasting results. Curative habitualpattern may include a curative exercise pattern, which may includewithout limitation an exercise schedule and/or plan for improvement ofhealth. Curative habitual pattern may include a pattern of meditationand/or spiritual practices, a pattern of therapy, a pattern ofconsultation with an advisor, therapist, family member, or the like, orany other pattern of behavioral habits that may act to improve, prevent,and/or alleviate one or more disease states.

Continuing to refer to FIG. 1, computing device 104 is configured togenerate the curative habitual pattern by providing a plurality ofcurative habitual patterns; this may be accomplished, withoutlimitation, by forming a query using the at least a disease state; querymay include without limitation a label and/or plurality of labels fordisease states. Computing device 104 may retrieve a plurality ofintervention elements from an intervention listing datastore as afunction of the query. An “intervention element,” as used in thisdisclosure is a single habit that may be incorporated in a curativehabitual pattern; for instance, a single dietary rule such as withoutlimitation eating a certain number of servings of fruit per day, eatingfewer than a certain number of grams of sugar per day, getting at least20 minutes of cardiovascular exercise per day, or the like. Eachintervention element may be represented by a plurality of impact scoresassociated with the intervention element, which may be listed in recordsin intervention database 136. Intervention database 136 may beimplemented in any manner suitable for implementation of expert database116 as described above.

Still referring to FIG. 1, impact scores associated with interventionelements, for instance as stored in intervention listing datastore, maybe populated by expert submission received as described above, includingwithout limitation records populated within disease alleviation table204. Alternatively or additionally, population of impact scores inintervention listing datastore may be performed using training datacorrelating intervention elements and/or labels thereof with impactscore quantities, for instance based on expert inputs and/or data froman expert database 116 as described above, such as without limitationfrom disease alleviation table 204, and/or using data records collectedin iterations of methods described in this disclosure, related processesperformed by system 100, and/or information entered by one or more usersdescribing outcomes of one or more methods as described herein whenpracticed by users. Training data may be used to train an interventionimpact score model using any machine-learning process as describedabove, to which computing device 104 may input user physiological datareceived from instant user to receive from the model impact scorescorresponding to intervention elements. Intervention elements and/orimpacts thereof may alternatively or additionally be identified asdescribed in U.S. Nonprovisional application Ser. No. 16/502,835.

Still referring to FIG. 1, computing device 104 may combine theplurality of intervention elements to form a plurality of curativehabitual pattern candidates. Computing device 104 may combineintervention elements into curative habitual pattern candidates byaggregating vectors of impact scores of intervention elements;aggregation may be performed using any suitable method for aggregation,including component-wise addition followed by normalization,component-wise calculation of arithmetic means, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which multiple intervention elements may becombined to create a vector associated with a curative habitual patterncandidate. Computing device 104 may implement one or more rulespreventing combination of mutually exclusive and/or overlappingintervention elements. For instance, and without limitation, anintervention element for an exercise program may be identified with anexercise program identifier and may be prevented from combination withanother element having the same flag; other categories of interventionelement may similarly be flagged and prevented from combination, suchthat nutritional interventions, for instance, may be treated as mutuallyexclusive. Valid combinations within categories may be populated inintervention listing datastore by processes as described above, forinstance by inclusion in training data, intervention listing datastoreand/or machine-learning model inputs under labels identifying suchcombinations.

Further referring to FIG. 1, computing device 104 may calculate acurative impact score of each curative habitual pattern candidate of theplurality of curative habitual pattern candidates. A “curative impactscore,” as used in this disclosure, is a numerical quantity indicating adegree to which aggregated intervention element scores making up avector thereof corresponding to curative habitual pattern candidatemaximizes overall improvement to lifespan and quality of lifeanalogously to reductions thereof represented by outputs of firstobjective function 128. Calculating curative impact score may includegenerating a second objective function 140 of the plurality of curativehabitual pattern candidates; second objective function 140 may beimplemented in any manner suitable for implementation of first objectivefunction 128 as described above. Generating second objective function140 may include receiving curative training data. Curative training datamay include a plurality of entries, each entry correlating a curativehabitual pattern candidate with at least a curative impact element.Computing device 104 may train a curative machine-learning model as afunction of the curative training data and a machine-learning process,wherein the curative machine-learning model inputs curative habitualpatter candidates and outputs curative impact vectors, each curativeimpact vector comprising at least a curative impact element; this may beperformed as described above for calculation of intervention elementimpact scores. Objective function may be a function of curative impactvectors. In other words, computing device 104 may generate secondobjective function 140 as an objective function of the curative impactvectors. Second objective function 140 may be formulated, withoutlimitation as a linear and/or mixed-integer objective function, and/oras a loss function; constraints for optimization may be implemented asdescribed above for constraints for first objective function 128.Computing device 104 may optimize second objective function 140; thismay be implemented in any manner suitable for optimization of firstobjective function 128 as described above.

Still referring to FIG. 1, computing device 104 is further configured toselect curative habitual pattern from plurality of curative habitualpattern candidates as a function of curative impact score. For instance,and without limitation, computing device 104 may rank curative habitualpattern candidates according to curative impact scores and select ahighest ranking curative habitual pattern candidate. A user may enter aninstruction indicating that user is not going to perform selectedcurative habitual pattern; computing device 104 may display to user anext highest ranking curative habitual pattern, which may be iterativelyrepeated until user enters an instruction indicating that user will usea selected curative habitual pattern 144. As a further example,computing device 104 may present curative habitual patterns in rankorder to user; computing device 104 may receive user selection of a usercurative habitual pattern candidate and select curative habitual patternas a function of the user selection.

Referring now to FIG. 3, an exemplary embodiment of a method 300 ofconstitutional analysis using first objective functions 128, isillustrated. At step 305, a computing device 104 generates a ranked list108 of diseases; this may be implemented, without limitation, asdescribed above in reference to FIGS. 1-2. Generating may includedetermining a plurality of disease impact score vectors associated witha plurality of diseases, each disease impact score vector including atleast a disease impact score. Determining plurality of impact scorevectors may include receiving disease impact training data 112, whichthe disease impact training data 112 including a plurality of entriescorrelating diseases of the plurality of diseases with disease impactscores, training a disease impact model 124 as a function of the diseaseimpact training data 112 and a machine-learning process, and determiningthe plurality of impact score vectors as a function of the diseaseimpact model 124. Computing device 104 generates a first objectivefunction 128 of the impact score vectors; this may be implemented,without limitation, as described above in reference to FIGS. 1-2. Firstobjective function 128 may include a linear objective function. Firstobjective function 128 may include a mixed integer objective function.Computing device 104 ranks the diseases according to optimization of thefirst objective function 128; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-2. Plurality ofuser physiological history data may include user-reported data.Plurality of user physiological history data may include biologicalextraction data.

At step 310, computing device 104 receives, from a user, a plurality ofuser physiological history data; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-2. At step 315,computing device 104 identifies, as a function of a disease stateclassifier 132, a plurality of disease states associated with pluralityof user physiological history data; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-2. Identifyingplurality of disease states may include determining an age of onset ofeach disease state of the plurality of disease states. Identifyingplurality of disease states may include determining a likelihood of eachdisease state of the plurality of disease states. Computing device 104may weight each element of the ranked list 108, wherein weightingfurther includes multiplying a likelihood of a disease statecorresponding to an entry in the ranked list 108 by an output of firstobjective function 128 for a corresponding disease of plurality ofdiseases.

At step 320, and with continued reference to FIG. 3, computing device104 matches at least a disease state of plurality of disease states toranked list 108 of diseases; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-2. At step 325,computing device 104 generates a curative habitual pattern to alleviatethe at least a disease state; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-2. Generating mayinclude forming a query using the at least a disease state, retrieving,from an intervention listing datastore, a plurality of interventionelements as a function of the query, combining the plurality ofintervention elements to form a plurality of curative habitual patterncandidates, calculating a curative impact score of each curativehabitual pattern candidate of the plurality of curative habitual patterncandidates, and selecting the curative habitual pattern from theplurality of curative habitual pattern candidates as a function of thecurative impact score. Calculating curative impact score may includegenerating a second objective function 140 of the plurality of curativehabitual pattern candidate and optimizing the second objective function140. Generating second objective function 140 may include receivingcurative training data, the curative training data including a pluralityof entries, each entry correlating a curative habitual pattern candidatewith at least a curative impact element, training a curativemachine-learning model as a function of the curative training data and amachine-learning process, wherein the curative machine-learning modelinputs curative habitual patter candidates and outputs curative impactvectors, each curative impact vector comprising at least a curativeimpact element, and generating the second objective function 140 as anobjective function of the curative impact vectors.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 4 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 400 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 400 includes a processor 404 and a memory408 that communicate with each other, and with other components, via abus 412. Bus 412 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 404 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 404 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 404 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 408 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 416 (BIOS), including basic routines that help totransfer information between elements within computer system 400, suchas during start-up, may be stored in memory 408. Memory 408 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 420 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 408 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 400 may also include a storage device 424. Examples of astorage device (e.g., storage device 424) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 424 may be connected to bus 412 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 424 (or one or morecomponents thereof) may be removably interfaced with computer system 400(e.g., via an external port connector (not shown)). Particularly,storage device 424 and an associated machine-readable medium 428 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 400. In one example, software 420 may reside, completelyor partially, within machine-readable medium 428. In another example,software 420 may reside, completely or partially, within processor 404.

Computer system 400 may also include an input device 432. In oneexample, a user of computer system 400 may enter commands and/or otherinformation into computer system 400 via input device 432. Examples ofan input device 432 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 432may be interfaced to bus 412 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 412, and any combinations thereof. Input device 432 mayinclude a touch screen interface that may be a part of or separate fromdisplay 436, discussed further below. Input device 432 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 400 via storage device 424 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 440. A network interfacedevice, such as network interface device 440, may be utilized forconnecting computer system 400 to one or more of a variety of networks,such as network 444, and one or more remote devices 448 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 444,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 420,etc.) may be communicated to and/or from computer system 400 via networkinterface device 440.

Computer system 400 may further include a video display adapter 452 forcommunicating a displayable image to a display device, such as displaydevice 436. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 452 and display device 436 may be utilized incombination with processor 404 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 400 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 412 via a peripheral interface 456. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions, and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for constitutional analysis usingobjective functions, the system comprising a computing device, thecomputing device configured to: generate a ranked list of diseases,wherein generating further comprises: determining a plurality of diseaseimpact score vectors associated with a plurality of diseases by:receiving disease impact training data, the disease impact training dataincluding a plurality of entries correlating diseases of the pluralityof diseases with disease impact scores and wherein receiving furthercomprises generating a user classifier, wherein the user classifier isconfigured to input user demographic data associated with a user andoutput disease impact training data; training a disease impact model asa function of the disease impact training data and a machine-learningprocess; and determining the plurality of impact score vectors as afunction of the disease impact model, wherein the plurality of diseaseimpact score vectors includes a disease impact score vector thatassesses a degree of susceptibility of improvement; generating a firstobjective function of the impact score vectors; and ranking the diseasesaccording to optimization of the first objective function; receive, fromthe user, a plurality of user physiological history data; identify, as afunction of a disease state classifier, a plurality of disease statesassociated with the plurality of user physiological history data and,for each disease state, estimate a likelihood that the disease state ispresent in the user; match at least a disease state of the plurality ofdisease states to the ranked list of diseases; and generate a curativehabitual pattern to alleviate the at least a disease state, whereingenerating further comprises: forming a query using the at least adisease state; retrieving, from an intervention listing datastore, aplurality of intervention elements as a function of the query; combiningthe plurality of intervention elements to form a plurality of curativehabitual pattern candidates; calculating a curative impact score of eachcurative habitual pattern candidate of the plurality of curativehabitual pattern candidates by: generating a second objective functionof the plurality of curative habitual pattern candidates, whereingenerating the second objective function further comprises: receivingcurative training data, the curative training data including a pluralityof entries, each entry correlating a curative habitual pattern candidatewith at least a curative impact element; training a curativemachine-learning model as a function of the curative training data and amachine-learning process,  wherein the curative machine-learning modelinputs curative habitual pattern candidates and outputs curative impactvectors; and generating the second objective function as an objectivefunction of the curative impact vectors optimizing the second objectivefunction; and selecting the curative habitual pattern from the pluralityof curative habitual pattern candidates as a function of the curativeimpact score.
 2. The system of claim 1, wherein the first objectivefunction further comprises a linear objective function.
 3. The system ofclaim 1, wherein the first objective function further comprises a mixedinteger objective function.
 4. The system of claim 1, wherein theplurality of user physiological history data includes biologicalextraction data.
 5. The system of claim 1, wherein the computing deviceis configured to identify the plurality of disease states by determiningan age of onset of each disease state of the plurality of diseasestates.
 6. The system of claim 1, wherein a disease state of the user,matched to the ranked list of diseases, is a cardiovascular diseasestate, and selecting the curative habitual pattern comprises selecting aplan for getting a predetermined minimum number of minutes ofcardiovascular exercise per day.
 7. The system of claim 1, wherein thecomputing device is further configured to weight each element of theranked list, wherein weighting further comprises multiplying alikelihood of a disease state corresponding to an entry in the rankedlist by an output of the first objective function for a correspondingdisease of the plurality of diseases.
 8. The system of claim 1, whereinthe computing device is further configured to optimize the firstobjective function by minimizing a loss function.
 9. The system of claim8, wherein the loss function is minimized by: assigning a variablerelated to a set of parameters, wherein the variables correspond to thedisease impact vectors; calculating an output of mathematical expressionusing the variable; and selecting a disease impact vector that producesan output having the lowest size.
 10. The system of claim 1, wherein theplurality of user physiological history data comprises aself-assessment.
 11. A method of constitutional analysis using objectivefunctions, the method comprising: generating, by a computing device, aranked list of diseases, wherein generating further comprises:determining a plurality of disease impact score vectors associated witha plurality of diseases by: receiving disease impact training data, thedisease impact training data including a plurality of entriescorrelating diseases of the plurality of diseases with disease impactscores and wherein receiving further comprises generating a userclassifier, the user classifier configured to input user demographicdata associated with a user and output disease impact training data;training a disease impact model as a function of the disease impacttraining data and a machine-learning process; and determining theplurality of impact score vectors as a function of the disease impactmodel, wherein the plurality of disease impact score vectors includes adisease impact score vector that assesses a degree of susceptibility ofimprovement; generating a first objective function of the impact scorevectors; and ranking the diseases according to optimization of the firstobjective function; receiving, by the computing device and from theuser, a plurality of user physiological history data; identifying, bythe computing device and as a function of a disease state classifier, aplurality of disease states associated with the plurality of userphysiological history data and, for each disease state, estimating alikelihood that the disease state is present in the user; matching, bythe computing device, at least a disease state of the plurality ofdisease states to the ranked list of diseases; and generating, by thecomputing device, a curative habitual pattern to alleviate the at leasta disease state, wherein generating further comprises: forming a queryusing the at least a disease state; retrieving, from an interventionlisting datastore, a plurality of intervention elements as a function ofthe query; combining the plurality of intervention elements to form aplurality of curative habitual pattern candidates; calculating acurative impact score of each curative habitual pattern candidate of theplurality of curative habitual pattern candidates by: generating asecond objective function of the plurality of curative habitual patterncandidates, wherein generating the second objective function furthercomprises: receiving curative training data, the curative training dataincluding a plurality of entries, each entry correlating a curativehabitual pattern candidate with at least a curative impact element;training a curative machine-learning model as a function of the curativetraining data and a machine-learning process,  wherein the curativemachine-learning model inputs curative habitual pattern candidates andoutputs curative impact vectors; and generating the second objectivefunction as an objective function of the curative impact vectors; andoptimizing the second objective function; and selecting the curativehabitual pattern from the plurality of curative habitual patterncandidates as a function of the curative impact score.
 12. The method ofclaim 11, wherein the first objective function further comprises alinear objective function.
 13. The method of claim 11, wherein the firstobjective function further comprises a mixed integer objective function.14. The method of claim 11, wherein the plurality of user physiologicalhistory data includes biological extraction data.
 15. The method ofclaim 11, wherein identifying the plurality of disease states furthercomprises determining an age of onset of each disease state of theplurality of disease states.
 16. The method of claim 11, whereinselecting the curative habitual pattern comprises selecting a plan forgetting a predetermined minimum number of minutes of cardiovascularexercise per day.
 17. The method of claim 11, further comprisingweighting each element of the ranked list, wherein weighting furthercomprises multiplying a likelihood of a disease state corresponding toan entry in the ranked list by an output of the first objective functionfor a corresponding disease of the plurality of diseases.
 18. The methodof claim 11, further comprising optimize the first objective function byminimizing a loss function.
 19. The method of claim 18, furthercomprising minimizing the loss function by: assigning a variable relatedto a set of parameters, wherein the variables correspond to the diseaseimpact vectors; calculating an output of mathematical expression usingthe variable; and selecting a disease impact vector that produces anoutput having the lowest size.
 20. The method of claim 11, wherein theplurality of user physiological history data comprises aself-assessment.